Untargeted plasma metabolome identifies biomarkers in patients with extracranial arteriovenous malformations

Xueqiang Fan; Xixi Gao; Yisen Deng; Bo Ma; Jingwen Liu; Zhaohua Zhang; Dingkai Zhang; Yuguang Yang; Cheng Wang; Bin He; Qiangqiang Nie; Zhidong Ye; Peng Liu; Jianyan Wen

doi:10.3389/fphys.2023.1207390

Untargeted plasma metabolome identifies biomarkers in patients with extracranial arteriovenous malformations

Front Physiol. 2023 Sep 1:14:1207390. doi: 10.3389/fphys.2023.1207390. eCollection 2023.

Authors

Xueqiang Fan^{1

2}, Xixi Gao^{1

2}, Yisen Deng¹, Bo Ma¹, Jingwen Liu¹, Zhaohua Zhang¹, Dingkai Zhang^{1

2}, Yuguang Yang¹, Cheng Wang³, Bin He¹, Qiangqiang Nie¹, Zhidong Ye¹, Peng Liu^{1

2}, Jianyan Wen^{1

2}

Affiliations

¹ Department of Cardiovascular Surgery, China-Japan Friendship Hospital, Beijing, China.
² Graduate School of Peking Union Medical College, Beijing, China.
³ Department of Pediatrics, Herman B Wells Center for Pediatric Research, Indiana University School of Medicine, Indianapolis, IN, United States.

Abstract

Objective: This study aimed to investigate the plasma metabolic profile of patients with extracranial arteriovenous malformations (AVM). Method: Plasma samples were collected from 32 AVM patients and 30 healthy controls (HC). Ultra-high performance liquid chromatography-mass spectrometry (UHPLC-MS) was employed to analyze the metabolic profiles of both groups. Metabolic pathway enrichment analysis was performed through Kyoto Encyclopedia of Genes and Genomes (KEGG) database and MetaboAnalyst. Additionally, machine learning algorithms such as Least Absolute Shrinkage and Selection Operator (LASSO) and random forest (RF) were conducted to screen characteristic metabolites. The effectiveness of the serum biomarkers for AVM was evaluated using a receiver-operating characteristics (ROC) curve. Result: In total, 184 differential metabolites were screened in this study, with 110 metabolites in positive ion mode and 74 metabolites in negative mode. Lipids and lipid-like molecules were the predominant metabolites detected in both positive and negative ion modes. Several significant metabolic pathways were enriched in AVMs, including lipid metabolism, amino acid metabolism, carbohydrate metabolism, and protein translation. Through machine learning algorithms, nine metabolites were identify as characteristic metabolites, including hydroxy-proline, L-2-Amino-4-methylenepentanedioic acid, piperettine, 20-hydroxy-PGF2a, 2,2,4,4-tetramethyl-6-(1-oxobutyl)-1,3,5-cyclohexanetrione, DL-tryptophan, 9-oxoODE, alpha-Linolenic acid, and dihydrojasmonic acid. Conclusion: Patients with extracranial AVMs exhibited significantly altered metabolic patterns compared to healthy controls, which could be identified using plasma metabolomics. These findings suggest that metabolomic profiling can aid in the understanding of AVM pathophysiology and potentially inform clinical diagnosis and treatment.

Keywords: arteriovenous malformations; biomarkers; metabolomic; plasma; untargeted.

Grants and funding

This research was funded by grants from the National Natural Science Foundation of China (Nos. 82270443, 81670275, 81670443, and 82170066), National High Level Hospital Clinical Research Funding (2022-NHLHCRF-ZSYX-01), and the International S&T cooperation program (2013DFA31900).